Integrative genomic profiling identifies MLPH as a candidate gene in prostate cancer.

Background: Prostate cancer (PCa) is a highly heterogeneous malignancy with complex genetic underpinnings. This study integrates multi-omics data to prioritize candidate susceptibility genes and evaluate their functional and clinical significance in PCa pathogenesis. Materials and methods: We integrated PCa GWAS summary statistics with GTEx v8 expression quantitative trait locus reference panels to perform cross-tissue and single-tissue transcriptome-wide association studies. Candidate signals were refined using conditional analysis, MAGMA and fastBAT gene-level tests, Summary data-based Mendelian randomization, and Bayesian colocalization. Tumor-context cis-eQTL evidence from TCGA-PRAD was incorporated to prioritize regulatory signals retained in prostate cancer tissues. Prioritized candidates were further assessed using transcriptomic datasets, Human Protein Atlas immunohistochemistry, single-cell RNA-seq analysis, histological grading, preoperative PSA, and established genomic risk signatures. Gene network and pathway enrichment analyses were performed to explore potential biological context. Results: The integrative genetic analyses identified 23 consensus candidate genes supported by multiple association frameworks. SMR and Bayesian colocalization further narrowed the candidate list, and tumor-context cis-eQTL analysis in TCGA-PRAD retained Discussion and conclusion: This integrative genomic analysis prioritizes